JOINT RESEARCH CENTER FOR PANEL STUDIESSELECTED DISCUSSION PAPER SERIESSDP2012-008
March, 2013

The Effect of Maternal Employment in Early Childhood on theChild’s Later Development

Kayo Nozaki*【Abstract】
This study aims to examine the effect of maternal employment in early childhood on thechild’s later development (outcomes). Specifically, this study conducted an empiricalanalysis of how maternal employment in the child’s first three years affects (1) the
child’s test scores from elementary school grade 1 to junior high school grade 3(Mathematics, Japanese language, and Reasoning); (2) the child’s school scores asviewed by the parent; (3) the child’s problem behavior as viewed by the parent; (4) the
child’s prosocial behavior as viewed by the parent; and (5) the child’s QOL (quality oflife). The results suggest a significant negative correlation between maternal
employment in early childhood and the child’s test scores. However, the results showthat maternal employment in early childhood have hardly any effect upon the child’s
school scores as viewed by the parent, problem behavior and prosocial behavior as
viewed by the parent, and QOL. Furthermore, the results also show that the negative
correlation between maternal employment in early childhood and the child’s test scores
diminish in higher school grades and that by the stage of junior high school, there is nocorrelation.

* Keio Advanced Research Centers(Panel Data Research Center),Keio University

Joint Research Center for Panel Studies
Keio University

The Effect of Maternal Employment in Early Childhood on theChild’s Later Development
Kayo Nozaki*
Version: March 2013

SummaryThis study aims to examine the effect of maternal employment in early childhood on
the child’s later development (outcomes). Specifically, this study conducted anempirical analysis of how maternal employment in the child’s first three years affects(1) the child’s test scores from elementary school grade 1 to junior high school grade 3(Mathematics, Japanese language, and Reasoning); (2) the child’s school scores asviewed by the parent; (3) the child’s problem behavior as viewed by the parent; (4) the
child’s prosocial behavior as viewed by the parent; and (5) the child’s QOL (quality oflife). The results suggest a significant negative correlation between maternal
employment in early childhood and the child’s test scores. However, the results showthat maternal employment in early childhood have hardly any effect upon the child’s
school scores as viewed by the parent, problem behavior and prosocial behavior as
viewed by the parent, and QOL. Furthermore, the results also show that the negative
correlation between maternal employment in early childhood and the child’s test scores
diminish in higher school grades and that by the stage of junior high school, there is nocorrelation.
* Keio Advanced Research Centers (Panel Data Research Center), Keio University,

Email: nozaki@a3.keio.jp

Section 1: Objective of Analysis†
According to the “14th Basic Survey of Birth Trends, National Survey on Marriage
and Births, Summary of Survey on Married Couples” by the National Institute ofPopulation and Social Security Research, the number of children per married couple in2010 (the number of children a married couple has after their last child is born) was
1.96. The report indicates that the number of children per married couple fell below
2.00 for the first time since the survey was first conducted in 1940. The number ofchildren is declining even among couples who have been in their marriage only for ashort period, suggesting that the declining birth rate is continuing.
A continued declining birth rate will lead to a reduced working-age population, which
will in turn result in a shortage of labor. One way of solving the problem of the labor
shortage is to utilize the potential female workforce. At present in Japan, theemployment rate for women peaks from their late 20s to their early 30s. It declines as
women enter the age of marriage and childbirth and then picks up again when women
are in their 40s and their children have grown up. This trend is depicted by the M-curveand is commonly known. Therefore, as part of the Cabinet Office’s “2012 White Paper
on Children and Childrearing,” the government has introduced targets for raising thecontinuous employment rate of women at the time they have their first child from 45%,

† The author is grateful to the 21st Century Center of Excellence Program at Keio

University and the Joint Research Center for Panel Studies at Keio University for
generously providing us with the data (Keio Household Panel Survey, Japan Household
Panel Survey and Japan Child Panel Survey). The author is also grateful to JSPS
(Young Researcher B) for financial support. We would like to thank Yoshio Higuchi,
Hideo Akabayashi, Miki Seko, Nobuko Nagase, Isamu Yamamoto, Yukako Ono, Chizuru
Shikishima, Kazuyasu Sakamoto and the participants of GCOE seminars and workshop
for helpful comments and guidance. Of course, all remaining errors are ours.
2the figure in 2012, to 55% by 2017.
However, the gender role attitudes whereby the husband works outside and the wifestays at home remains deeply rooted in Japan, as in maternal affection mythology andthree-year-old-child mythology (i.e., “children should be raised by the mother”). This is
one factor that inhibits women from participating in the workforce. According to theOrganization for Economic Cooperation and Development (OECD) (2012), the wage
gap between men and women in Japan was the second largest, behind that of South
Korea. In particular, the wage differentials between working mothers and working
fathers are large, presenting a very severe situation for working mothers. According to
the National Institute of Population and Social Security Research’s “14th Basic Surveyof Birth Trends, National Survey on Marriage and Births, Summary of the Results ofSurvey on Married Couples,” the percentage of married women who responded “agreecompletely” when presented with the statement, “it is good for women not to work andto stay at home at least during the child’s early years” declined dramatically from 47.9%
in 1992 to 17.9% in 2010. However, the combined percentage of married women who
answered “agree completely” to the above question and married women who answered“somewhat agree,” although it was lower than it had been in 1992, was still very high in2010—69.5% of the entire sample of married women. This reveals that many couples
still do not have a favorable image of mothers working during the child’s early years.
Matsuda (2005) has examined the attitude that a preschool child is likely to suffer if hisor her mother works. Matsuda found that this attitude remains firmly rooted,
particularly among the generation that raised children before the 1970s, and that this3

attitude fades away in younger generations and among couples in which the wife is
working.
Ohinata (2006) discusses the context in which such maternal affection mythology
spread in Japan. In addition to the expansion of gender role attitudes in the 1950s andthe socioeconomic background during the period of low economic growth in the 1970s
that emphasized the role of mothers, Ohinata points out an underlying bias resulting
from the introduction of the findings of overseas psychological and pediatrics studies
that emphasized the message that the “absence of the mother will negatively impact achild’s development.” In particular, pediatrics in Japan borrowed heavily from theresearch results of the British psychiatrist John Bowlby (1951). The World HealthOrganization (WHO) commissioned Bowlby to conduct a series of studies. Bowlby
studied the development of children who had spent time apart from their families in
hospitals or care facilities. He reported that the development of these children wasconsiderably delayed compared with children who had not been separated in this way
from their families. As one way of explaining this, Bowlby developed the attachment
theory (Bowlby, 1969). Attachment here refers to children approaching and seeking
contact with specific individuals. Attachment behavior refers in this case to various
behaviors shown by children in order to approach and contact these specific individuals.
Attachment theory is an attempt to explain both the attachment behavior that sometimes
surfaces and sometimes disappears and also the continuous attachment that children andother individuals show toward specific subjects. By clinging to specific individuals,
children who have just been born exercise behavior (attachment behavior) in which they4

seek others and attempt to be close to others. The reason children of up to three months
smile at anyone is they have not yet identified specific individuals to be attached to.
After this period, children become shy of strangers and begin to seek attention fromonly certain individuals. This signifies that these specific individuals have been fixed.
Children then show attachment behavior toward these individuals, especially when they
feel pain in times of sickness, etc. During infancy, a child’s lacking or being deprived of
attachment will increase the child’s pain and anxiety and will negatively impact thechild’s development. Bowlby’s above findings are discussed in research concerning
hospitalism, defined as the negative physical and psychological impact resulting frombeing sent to a hospital or care facilities. As previously mentioned, the person to whoma child is attached is a specific person, but there is no mention that it must be the mother.
However, according to Ohinata (2006), when Bowlby’s findings were introduced inJapan, the message that the absence of mothers would negatively impact the child wasemphasized above all else. This is considered to be a factor that led to the three-year-old
mythology taking root in Japan.
In the National Longitudinal Survey of Youth (NLSY), a US longitudinal follow-up
study, Harvey (1999) examined six research findings to determine the impact of amother’s participation in the workforce while her child is less than three years old on
the child’s development. She points out the lack of consistency in the results—for
example, she found that mothers’ long working hours during the first three years of thechild’s life had a slight negative impact on children’s academic competence at age sevenand their intellectual capacity at age nine. Moreover she found that there was a lack of5

correlation with problem behavior, and a rise in income could positively impact achild’s development. Sugawara (2005) conducted a follow-up survey in Japan ranging
from the time the child was in the womb until the age of 15 or so. Sugawara
investigated whether a child’s development until age 14 was affected by the mother’s
returning to work before the child was three years old. The results showed that there
was no relationship between a mother’s returning to work before the child was three
years old and the child’s problem behavior and depression tendencies.
In the field of economics as well, particularly in US and Europe, the topic has been
discussed from the perspective of mothers’ participation in the workforce, and there
have been various arguments concerning how maternal employment in early childhood
effects the child’s educational results through the process of academic competency
formation. Based on these research findings, it is noted that by working, a mother hasless contact time with her child, and this may deter the child’s development. It is also
pointed out that by working, a mother can save for the costs of her child’s education,
allowing her to invest more into the child’s education, which could improve the child’sdevelopment (Baum, 2003; Berger, Hill, and Waldfogel, 2005; Bernal et al., 2008). For
example, Baum (2003) analyzed how maternal employment in early childhood effected
the child’s cognitive development. The results show that a mothers’ employment during
the first three months of the child’s life has a negative impact on the child’s cognitive
development. On the other hand, the negative effect on cognitive development inchildren whose mothers participate in the labor market is partially offset by a positiveeffect on cognitive development in the children of households with increased income

6from the mother’s participation in the labor market.
As for studies conducted in Japan, Tanaka (2008) used the Japanese General Social
Surveys (JGSS) to analyze the impact of a mother’s work situation while her child is 15
years old on the child’s final academic background. The results showed that in cases
where the mother is a non-regular employee or self-employed while her child is 15
years old, her child, whether son or daughter, will have a poorer academic record than incases where the mother is a full-time housewife. However, in cases where the mother isa regular employee, the tendency is that only the son will have a poorer academic
background. The results also showed that daughters of mothers who are regular
employees, if they themselves become mothers, tend to also work as regular employees.
Tanaka and Yamamoto (2009) used the Japan Parent-Child Survey, which was
conducted in the Osaka University 21st Century COE Project, to analyze the impact ofmaternal employment in infancy and early childhood on the likelihood that the childwould be admitted into private or state junior high schools. The results show thatmaternal employment while the child is in elementary school negatively impacts thelikelihood of the child going on to private or state junior high schools, but it shows thatmaternal employment while the child is under six years old, which includes
employment while the child is zero to three years old, has no impact.
Sakamoto (2009) analyzed children’s development not only in terms of the impact
from the mother but also of how a child’s development is affected by household
attributes. Specifically, Sakamoto examined whether there was a difference in children’sdevelopment (academic background attained, first job, physical and psychological pain7

scale, and whether the child goes on to have children at an early age) between single
parent households or households in which the mother gave birth at a young age andhouseholds with no such attributes. Sakamoto conducted this analysis using theJapanese Panel Survey of Consumers, which is conducted by the Institute for Researchon Household Economics. Regarding childbirth at an early age, the results revealed thatchildren born of young mothers tended to have poor academic backgrounds and that
their first job was highly likely to be non-regular. The results also revealed a hightendency for such children to have children early themselves. Children of single parent
households were also revealed to have a tendency toward lower academic achievement.
Akabayashi et al. (2012) and Shikishima et al. (2012) used the JCPS, which is
conducted by the Joint Research Center for Panel Studies at Keio University, to conduct
factor analysis on children’s academic competence, sociability, and adaptability. The
results showed that maternal employment at the time of the survey had little impact onchildren’s academic competence and sociability at the time of the survey.
However, among the studies conducted in Japan, there are few analyses that shedlight on the impact that maternal employment in early childhood (0–3 years old) has on
outcomes such as the child’s school results. This study makes use of the JCPS,
conducted by the Joint Research Center for Panel Studies at Keio University, where
academic test results are available. The study then conducts an examination into howmaternal employment in early childhood impacts the child’s outcomes at the level of
elementary school and junior high school (academic test results, problem behavior,prosocial behavior, and QOL). Elementary school and junior high school represent the8period of compulsory education, and virtually all children receive such education.
Moreover, by the time they are in elementary and junior high school, children are able
to answer questions by themselves regarding academic tests and other aspects of theirschool life. Therefore, as the first step in investigating the impact of maternal
employment in early childhood (0–3 years old) on the child’s outcomes, we focused theanalysis on children’s outcomes during elementary and junior high school education.

Section 2: Data and Analysis Method
1. Data
The data used in this study came from the 2011 and 2012 surveys of the Japan Child
Panel Survey (JCPS) conducted by the Joint Research Center for Panel Studies at Keio
University. The 2011 Keio Household Panel Survey (KHPS 2011) and the 2012 Japan
Household Panel Survey (JHPS 2012) were both conducted in households with children
in elementary school grade 1 to junior high school grade 3. It was therefore possible touse the information from both the KHPS and JHPS.i Because the KHPS and the JHPS
target different subjects, the data were pooled and utilized not as panel data but as
cross-section data. In addition, because the surveys are conducted on all children fromelementary school grade 1 to junior high school grade 3, they also include children(siblings) from the same households so that information about parents is the same.
Furthermore, because the focus was on maternal employment in early childhood, single
father households (motherless families) were removed from the sample.
i

The JCPS is conducted as a supplementary survey to the JHPS and KHPS, in alternate years. It was
conducted alongside the KHPS in 2011 and conducted alongside the JHPS in 2012. This study therefore
used the KHPS 2011 and JHPS 2012 that coincided with the years of the JCPS survey.

9

We gathered retrospective data included in the KHPS and JHPS to prepare
information on the employment status of mothers during the child’s early years. Boththe KHPS and JHPS inquired into the forms of employment the study subjects have hadin the past. The subjects and their spouses were each asked whether their employment
status was “regular worker,” “non-regular worker,” “self-employed,” “worker at familybusiness,” or “working at home” after age 18.ii This information together with thehousehold information—child’s age and mother’s age at the child’s birth—enabled us todiscern for each child the employment status of the child’s mother during the child’sbirth and her employment status after the child’s birth. Graph 2-1 shows the change inmother’s employment status after each child was born. Because the retrospective dataon employment status could only be obtained per year, attention should be paid to thefact that employment status during year of birth fails to discern whether the time period
in question was before birth or after birth. Looking at Graph 2-1, we see that thepercentage of mothers working in the year before birth was 54.4% but dropped to 41%
zero to one year after the child’s birth and then steadily began to rise after this. Whenviewing the data by employment status, the percentage of mothers who were regular
employees in the year before they gave birth was 30% of the entire sample, but thisfigure dropped after that and remained at roughly 13%. This suggests that many of themothers who were regular employees before giving birth left their employment after
giving birth and, furthermore, did not often return to work as regular employees evenii

In the retrospective data regarding employment status, there were some cases where the subjects gave
two different answers for the same period (for example, “non-regular worker” and “worker for familybusiness”). In such cases, only the status for what was likely the main employment was selected, based on
information such as employment status around the time of the survey.

10when their children were older. On the other hand, whereas the percentage of
non-regular worker fell when the children were zero to one, as the children grew older,the percentage of non-regular worker increased. In the case of self-employment, worker
at family business, and working at home, the percentage remained fixed. With
self-employment, family business employment, and working from home, there is a highpossibility that the mother can work in the same house even after giving birth (formother’s employment status, worker at family business and working at home hadparticularly high percentages), and as such the percentages for these types of
employment did not drop much after the children’s births. These tendencies wereconsistent with the results of the Health, Labour and Welfare Ministry’s “9thLongitudinal Survey of Babies in 21st Century (children born in 2001).”
Furthermore, Graph 2-2 shows the change in the employment status of just themother before and after the first child was born. Compared with Graph 2-1, which
includes subjects at the time of the birth of their second child and onwards, we see thatthe employment rate of mothers after their first child tended to be lower.

11

Graph 2-1: Change in Mother’s Employment Status (n=1,161)
0%

20%

Year
before…
Year of
birth

40%

30.0
18.5

60%

13.9
9.0

10.5

12.0

6.5

1-2 years

12.2

9.0

2-3 years

13.1

11.8

3-4 years

12.2

16.1

4-5 years

13.2

19.7

5-6 years

13.4

22.6

100%

45.7

13.5

0-1 year

80%

58.9

15.5

66.0

16.4

62.4

15.8

59.3

15.5

56.2

15.4

51.7

14.4

49.7

Regular worker
Non-regular worker
Self-employed, worker at family business or working at home
Housewife, unemployed, student, no answer

Graph 2-2: Change in Mother’s Employment Status (first child only, n = 555)
0%

20%

Year
before…

40%

60%

48.3

Year of
birth

27.2

18.4
10.5

80%
7.6

12.4

25.8

49.9

0-1 year

14.2

5.4

14.8

65.6

1-2 years

12.8

7.6

15.9

63.8

2-3 years

12.3

9.4

15.0

63.4

3-4 years

12.6

12.6

4-5 years

12.4

15.3

16.0

56.2

5-6 years

11.9

17.1

17.1

53.9

15.1

100%

59.6

Regular worker
Non-regular worker
Self-employed, worker at family business or working at home
Housewife, unemployed, student, no answer

12

The data used to show child’s outcomes are academic test scores, school’s scores asviewed by the parent, index of problem behavior, index of prosocial behavior, and index
of quality of life (QOL).
For test scores, the test scores from Mathematics, Japanese language, and Reasoning
are used. In this analysis, one point is assigned to each question in each subject (math,
Japanese language). The sums of these were standardized so that a mean score both byschool grade and by subject would be 50 with a standard deviation of 10. Sum scoring
was used for reasoning, because there were only four reasoning questions.
Regarding the school’s scores as viewed by the parent, the parents of each child wereasked about their child’s scores in Japanese language, math, and English language (onlyjunior high school grade 2 and above) for the school year that finished in March. The
parents gave answers on a five-point scale ranging from best scores to worst scores.
This analysis reversed the scoring so that 5 represented the best scores and 1 represented
the worst scores.
The Strengths and Difficulties Questionnaire (SDQ) index is used for problem
behavior as viewed by the parent and prosocial behavior as viewed by the parent. The
data used for child’s problem behavior are the totals of the scores calculated for each of
five question items concerning four aspects, which were among the questions thatinquired into the state of the child as viewed by the parent. The four aspects are“emotional symptoms,” “conduct problems,” “hyperactivity/inattention,” and “peerproblems.” On this index, higher scores indicate a greater degree of problem behavior.
Questions inquiring into the state of the child as viewed by the parent are also used for13child’s prosocial behavior. The index is calculated from five question items such as“kind to younger children,” and higher scores indicated higher cooperativeness andempathy.
For a child’s QOL, answers given by the children are used (only elementary school
grade 3 and above). The scale used was the children-oriented KINDLR, which is a QOL
scale that measures children’s adaptability (physical and psychological well-being anddegree of satisfaction and fulfillment in social relations). The six sub-domains of“physical health,” “emotional well-being,” “self-respect,” “family,” “friends,” and“school” are used to assess the children’s QOL from multiple angles, and the total scorefor each domain represents the QOL score. On this index, higher scores indicate higher
adaptability.iii

2 Analysis Method
Regarding the analysis method, we attempted estimations using the following
formulas.
𝑌𝑖𝑘 = 𝑿′𝑖𝑘 𝒃𝑘 + 𝛼𝑘 𝐻𝑖𝑘 + 𝑒𝑖𝑘

(1)

∗
𝐻𝑖𝑘
= 𝑿′𝑖𝑘 𝝅𝑘 + 𝑾′𝒊𝒌 𝜸𝑘 + 𝑢𝑖𝑘 (2)𝐻𝑖𝑘 = {

1(Mother worked at least once during the child′ s first three years)
0 (Mother did not work even once during the child′ s first three years)

𝑌𝑖𝑘 is child’s (i) outcomes (k), and 𝐻𝑖𝑘 is the employment status of the mother during
the child’s early years. 𝑿𝑖𝑘 is the other explanatory variable vector, and 𝑾𝑖𝑘 is theiii

For more details about children’s problem behavior and prosocial behavior as viewed by parents, aswell as children’s QOL, see Shikishima et al. (2012).

14

explanatory variable vector concerning mother’s employment status during the child’s
early years. Estimation is conducted using ordinary least squares (OLS) and also using
the instrumental variables method (IV) and the treatment effect model (TEM). OLS
estimated by treating dummy variables that assigned a value of 1 to mothers who
worked at least once during their child’s first three years of life as exogenous variables
and then estimating only Formula (1) above. With IV and TEM, estimates use thedummy variables that assigned a value of 1 to mothers who worked at least once during
their child’s first three years of life as endogenous variables. There is the possibility ofendogeneity emerging in the relationship between maternal employment in earlychildhood and the child’s outcomes. For example, it could also be the case that mothers
who work during their children’s early years have a high degree of competency, and thechildren may also be affected by this competency. For this reason, instead of using OLS
alone, we also consider endogeneity, and based on that, we conduct first-stage
estimation to determine whether a mother is working during the child’s first three yearsof life (𝐻𝑖𝑘 ) and second-stage estimation to assess the relationship between maternal
employment in early childhood and the child’s outcomes. The dummy for maternal
employment in early childhood is a binary variable, so in the IV method, Formula (2)above, which is the first-stage estimation, is worked out by the linear probability model.
For TEM, working out Formula (1) and Formula (2) simultaneously by maximum
likelihood estimation enabled more effective quantification. Regarding the instrumental
variables, the variables corresponding with 𝑾𝑖𝑘 are the during the child’s first three
years, father’s employment status, number of siblings, whether the child was left in a15

nursery, and the number of working women in the residential area. iv The results
estimated through OLS, IV, and TEM are used to investigate the impact of maternal
employment in early childhood on the child’s outcomes.
In order to view the different impacts on the child’s outcomes of different types ofmaternal employment during the child’s early years, the case of continuous
unemployment during the child’s early years (full-time housewife) is set as the reference,and the impact of other types of employment on a child’s outcomes is examined.
Specifically, 11 categories are used in OLS estimation. These are “mother had
continuous regular employment for three years”; “mother had regular employment fromthe second year”; “mother had regular employment in the third year”; “mother had
continuous non-regular employment for three years”; “mother had non-regular
employment from the second year”; “mother had non-regular employment in the thirdyear”; “mother was self-employed, worked at the family business, or worked at home
continuously for three years”; “mother was self-employed, worked at the familybusiness, or worked at home from the second year”; “and mother was self-employed,
worked at the family business, or worked at home in the third year.” The IV estimation
and TEM that use the 11 categories could not be conducted because the calculation
became too complicated. Therefore, the estimation is conducted by OLS only.
In order to examine the impact of household income status on the child’s outcomes,
income of the household the child belonged to, the mother’s income, and theiv

The data used are the local female labor participation rate while children are zero to three from theLabour Force Survey. However, because it is not possible to distinguish former residential areas, the data
were taken from current residential area.

16

household’s educational expenses are used as explanatory variables 𝑿𝑖𝑘 .v In addition,
the dummies used are as follows: gender, which assigned girls a value of 1, birth in thelast three months of the school year, eldest child, two siblings, three siblings, four ormore siblings (one sibling is the reference), child’s age, mother’s age, mother’s
university degree, fatherless family, father’s university degree, number of students perteacher in the prefecture in which subjects reside,vi and JHPS (the descriptive statistics
use for the analysis are shown in Table 2-1).
In addition, child’s development, child’s educational expenses, and mean householdincome when the mother was continuously unemployed during the child’s early years(unemployed [continuously]) are compared with the same data in the case where themother was not continuously unemployed (Table 2-2). The results reveal that theachievement test results of children whose mothers worked at least once during the first
three years of the child’s life or had worked continuously as regular or non-regular
employees tended to be lower than those of children whose mothers were continuously
unemployed. On the other hand, the children of mothers who had worked tend to havehigher prosocial behavior and QOL. Educational expenses and household income tend
to be higher in cases where the mother had continuous regular employment and in cases
where the mother was self-employed, worked at the family business, or worked at
home.

v

This is the total sum spent on each child, which included money for extracurricular activities, school
fees, child’s allowance (average per month), and other (including end of year gifts).
vi
This valuable is not dummy and the data used from the School’s Basic Survey, which is conducted by
the Ministry of Education, Culture, Sports, Science and Technology.

17

Table 2-1: Descriptive Statistics

Girl
Born in the first three months of the year
Eldest child
One sibling
Two siblings
Three siblings
Four or more siblings
Child's age
Mother's age
Mother: University degree
Fatherless family
Father: University degree
JHPS
Number of students per teacher
During the child's early years
Day care nursery used
Father: In regular employment
Father: Self-employed
Rate of women working
One sibling
Two siblings
Three siblings
Four or more siblings
Sample size

Unemployed duringchild's early years

Employed duringchild's early years

Mean

SD

Mean

SD

0.5
0.2
0.5
0.1
0.5
0.3
0.1
11.0
41.7
0.1
0.02
0.4
0.4
16.3

0.5
0.4
0.5
0.3
0.5
0.5
0.2
2.5
4.9
0.4
0.2
0.5
0.5
2.1

0.5
0.2
0.5
0.1
0.5
0.3
0.1
10.8
41.6
0.2
0.1
0.4
0.4
16.2

0.5
0.4
0.5
0.3
0.5
0.5
0.3
2.5
5.2
0.4
0.2
0.5
0.5
2.2

0.05
0.9
0.1
48.7
0.3
0.5
0.1
0.02

0.2
0.3
0.2
2.1
0.5
0.5
0.4
0.1

0.5
0.8
0.2
48.9
0.3
0.5
0.2
0.04

0.5
0.4
0.4
2.1
0.5
0.5
0.4
0.2

568

18

476

Table 2-2: Child’s Development, Child’s Educational Expenses, and Household
Income by Mother’s Type of Employment during Child’s Early Years (type of
employment while the child is aged 0-3, the mean)
Mother's employment status during child's early years
Unemployed(continuously)
Variables related to child's development
Test score: Math*
Test score: Japanese language
Test score: Reasoning
Sample size
Results as seen by the parent: Math*
Sample size
Results as seen by the parent:
Japanese language
Sample size
Results as seen by the parent:
English language
Sample size
Problem behavior (0-30)
Sample size
Prosocial behavior (0-10)
Sample size
QOL (0-100)
Sample size
Extracurricular activities(average per month: ##yen)
School fees
(average per month: ##yen)*Child’s allowance(average per month: ##yen)*
Other (New Year's gifts etc.,
average per month: ##yen)
Sample size
Household income (1 year: ##hundred thousand yen)***Sample size

50.7
50.5
2.7
568
3.5
564
3.4

Employed

Regularworker(continuously)

Non-regular
worker(continuously)

49.2 **
49.7
2.7
476
3.4
473

48.7 **
48.9
2.7
105
3.5
104

50.5
47.1 **
2.7
40
3.4
40

Self-employed,
working at familybusiness(continuously)
50.3
51.6
2.8
133
3.5
133

Other

48.5 ***
49.4 *
2.5 **
198
3.3 **
196

3.4

3.3

3.4

3.5

3.3

472

104

39

133

196

3.1

3.2

3.0

3.5 *

2.9

138
8.4
474
6.4 *
474
69.5 **
368

37
8.7
105
6.4
105
68.4
80

15
7.8
40
6.1
40
71.7 *
32

35
8.2
131
6.6 **
131
69.1
109

51
8.6
198
6.3
198
69.8 *
147

6717

7755

9283 *

6368

7459

7423

11605

13624 *

15114 **

8600

15980 **

12266

897

995

1015

1110

1078 *

905

19945

21848

23943 *

27635 *

19468

21167

476

105

40

133

198

6.7

6.7

6.2 **

40

133

198

564
3.1
177
8.3
566
6.1
566
67.5
432

568
6.9
568

7.0

8.9 ***

476

105

Note) A “*” symbol next to a numeral denotes the results of testing the difference in average value when compared to the casewhere the mother is unemployed (continuously.) *** = p < 0.01, **= p < 0.05, *= p < 0.1.

Section 3: Results of Analysis
Table 3-1 shows the IV and TEM estimation results for math test scores. We first lookat the test scores for determining whether the maternal employment in early childhood
dummy (𝐻𝑖𝑘 ) is an endogenous variable. Looking at the results of the Wu-Hausman
test, it can not be rejected the null hypothesis that 𝐻𝑖𝑘 is an exogenous variable, andthe calculation concerning math test scores suggest the possibility that 𝐻𝑖𝑘 could be an
exogenous variable. On the other hand, although the test statistics are not included,
19

because the null hypothesis that 𝐻𝑖𝑘 is an exogenous variable can be rejected in thecalculation for Japanese language test scores at a level of 1%, in the reasoning scores at
a level of 5%, in the scores for Japanese language as viewed by the parent at a level of
1%, and in the child’s problem behavior as seen by the parent at a level of 5%, thesestatistics suggest that it is highly likely that 𝐻𝑖𝑘 is an endogenous variable.
Second, we look at the test scores concerning instrumental variables. The conditions
for instrumental variables are that there should be partial correlation between
endogenous and instrumental variables and no correlation between instrumental
variables and error terms in Formula (1). It is possible to use the first-stage F test (H0:coefficient of instrumental values is 0) to test whether there is a partial correlation
between endogenous and instrumental variables. The first stage F test concerning
mother’s employment during the child’s early years is above the standard of 10, so thecorrelation between instrumental and endogenous variables is not weak, and we judge
that these are not weak instruments. We also conduct an overidentification test in orderto test the relationship between instrumental variables and Formula (1) error terms. The
null hypothesis of the overidentification test is “none of the instrumental variables arecorrelated with error terms.” None of the test results allow this null hypothesis to be
rejected. In other words, we understand that the instrumental variables have nocorrelation with the error terms. The test results concerning the instrumental variables
show the same tendency in all of the IV estimations from Table 3-1 onwards. Thisreveals that it is highly likely that the instrumental variables use in the estimations havethe proper attributes of instrumental variables.

20

Looking at the estimation results concerning maternal employment in early life,
which is the first-stage estimation in Table 3-1, mothers who make use of nurseryschools are highly likely to be working. On the other hand, the likelihood that themother worked is lower when the father had regular employment. The likelihood thatthe mother worked is also lower when there is a second child. The results also revealthat the likelihood that the mother worked is higher when she has graduated fromuniversity but is lower in the case of the first child.
Table 3-2 shows the results obtain through OLS shown in Table 3-1 alongside part of
the results obtained through IV and TEM. Looking at the Table, we see that whenmother’s income is taken into account, the difference in continuous unemployment andmath scores disappear, but this difference remain with IV and TEM estimation, and themath scores are lower when the mother had worked during the child’s early years.vii
Table 3-3 shows the results of estimation concerning Japanese language test scores.
With OLS, Japanese language test results have no significant difference with continuous
unemployment, but when IV and TEM are used, Japanese language test scores are
significantly lower. The same can be seen for reasoning in Table 3-4—when
endogenous variables are taken into account, children of mothers who worked during
the child’s early years have significantly lower test scores than do children whose
mothers did not work during their early years.
Regarding the impact other variables have on test scores (math), as can be seen in
Table 3-1, test scores tend to be lower when the child was born in the last three months
vii

However, the results of the Wu-Hausman test used for the estimation in the math test results suggest
the possibility that maternal employment in early childhood dummy is not an endogenous variable.
Therefore, these results should be considered to support the OLS estimation results.

21

(from January to March) of the school year or had more than three siblings and tend tobe higher the older the mother is. The test scores also tend to be higher when bothparents had graduated from university. On the other hand, children raise in fatherlessfamilies have lower academic test scores. It is understood that there is a tendency
whereby the higher the household income, the higher math and Japanese language
scores. This confirms that if a household has higher income to invest into greater
learning resources, the child of that household will have higher test scores. On the other
hand, reasoning scores are not shown to be related to household income.
Table 3-5 shows the coefficients of the dummy variables that assigned a value of 1
when the mother had employment during the child’s early years, in terms of theestimations of school’s scores as viewed by the parent, problem behavior as viewed by
the parent, prosocial behavior as viewed by the parent, and the child’s QOL. School’s
scores as seen by the parent are not related to maternal employment in early childhood,
but problem behavior is higher with IV and TEM. Regarding the estimation for child’s
QOL, whereas QOL is higher when the mother had been employed during the child’s
early years according to OLS, this result does not significant when IV and TEM areused to take endogenous variables into account.
The above estimation results reveal that maternal employment in early childhood canpotentially lower the child’s future test scores. Similarly, when dividing maternal
employment in early childhood into the 11 categories based on types of employment
and then conducting estimation using OLS, the results show that compared withchildren whose mothers had been continuously unemployed, children of mothers who

22had had continuous regular employment from the time they were born havesignificantly lower test scores. Maternal employment in early childhood has apotentially negative impact on children’s development, in particular, their test scores.
Finally, in order to investigate how many school grades this impact extended to, theestimation is conducted separately by school grade group (Table 3-6, Table 3-7.) Table
3-6 shows the coefficients by school grade group (three school grades per group) properto the employment during the child’s first three years dummy. Table 3-7 shows thecoefficients by school grade group proper to the dummy when the mother had had
continuous regular employment during the child’s first three years. The results showthat in the lower school grades, academic test scores are negatively correlated withmaternal employment in early childhood but that the higher the school grade, theweaker this negative correlation, and the correlation disappear by junior high school.
Regarding child’s problem behavior, the results of estimation by school grade groupalso show no significant correlation with maternal employment in early childhood.

23

Table 3-1: Estimation Results of Math Test Scores(IV, TEM, n = 1,044)
IV
Math

Mother Employed duringchild's early years
Child's Gender(girl: 1)
Born in first three monthsof the year

-2.426**[1.137]-0.63[0.587]-2.142***[0.698]1.149[0.700]-1.402[0.999]-2.337**[1.104]-6.158***[1.555]-0.284**[0.140]0.232***[0.0811]1.876**[0.780]-4.019**[1.775]2.010***[0.660]-0.0867[0.154]0.137[0.613]0.00305***[0.00115]

Mother Employedduring child's early
years

Math

0.0428*[0.0259]-0.0152[0.0306]-0.0941***[0.0345]-0.0322[0.0561]-0.0373[0.0630]
0.0379[0.0844]
0.00755[0.00661]-0.00743**[0.00329]
0.0703*[0.0375]-0.0153[0.0766]-0.00733[0.0297]-0.000811[0.00633]
0.0226[0.0268]0.000092*[0.000048]-2.429**[1.137]-0.622[0.586]-2.159***[0.697]1.123[0.703]-1.341[0.999]-2.263**[1.101]-6.076***[1.557]-0.303**[0.140]0.229***[0.0813]
1.835**[0.778]-4.000**[1.773]2.003***[0.659]-0.0814[0.153]0.245[0.620]0.00293**[0.00114]
0.00865[0.00758]

TEM

Mother Employedduring child's early
years

Math

0.0438*[0.0258]-0.017[0.0307]-0.0973***[0.0344]-0.0255[0.0563]-0.0276[0.0632]
0.0467[0.0844]0.00552[0.00661]-0.00765**[0.00329]
0.0665*[0.0375]-0.00779[0.0765]-0.00888[0.0296]-0.000293[0.00628]
0.033[0.0271]0.0001[0.00005]
0.000863***[0.000281]-2.343*[1.357]-0.624[0.586]-2.163***[0.697]1.119[0.702]-1.338[1.000]-2.258**[1.103]-6.099***[1.560]-0.303**[0.140]0.230***[0.0813]1.850**[0.782]-3.956**[1.808]1.993***[0.656]-0.084[0.153]0.244[0.620]0.00300**[0.00123]
0.0086[0.00759]-0.00043[0.00275]

Mother Employedduring child's early
years

Math

-2.760**[1.085]-0.611[0.585]-2.145***[0.682]1.127[0.703]-1.433[1.075]-2.367**[1.183]-6.150***[1.499]-0.286**[0.143]0.231***[0.0739]
1.902**[0.840]-3.953**[1.646]1.982***[0.675]-0.0901[0.145]0.154[0.605]0.00309***[0.00104]

Mother Employedduring child's early
years

Math

0.150*[0.0899]-0.0417[0.105]-0.334***[0.122]-0.124[0.180]-0.136[0.212]0.131[0.269]0.0255[0.0228]-0.0239**[0.0119]0.249*[0.128]0.0366[0.304]0.00402[0.104]0.000892[0.0228]0.107[0.0940]0.000296*[0.000159]-2.781**[1.081]-0.602[0.585]-2.163***[0.682]1.099[0.704]-1.372[1.075]-2.292*[1.183]-6.065***[1.500]-0.306**[0.145]0.229***[0.0739]
1.861**[0.840]-3.930**[1.645]
1.973***[0.675]-0.0849[0.145]0.266[0.614]0.00297***[0.00104]
0.00896[0.00804]

Mother Employedduring child's early
years

Math

0.152*[0.0901]-0.0444[0.105]-0.345***[0.122]-0.0999[0.181]-0.0984[0.213]0.169[0.271]0.0183[0.0232]-0.0244**[0.0119]0.237*[0.129]0.0705[0.305]-0.00443[0.104]0.00287[0.0229]0.141[0.0955]
0.000251[0.000160]
0.00298**[0.00137]-2.862**[1.253]-0.599[0.585]-2.161***[0.682]1.099[0.704]-1.376[1.076]-2.298*[1.184]-6.050***[1.509]-0.307**[0.145]0.228***[0.0742]1.854**[0.845]-3.951**[1.666]
1.977***[0.676]-0.0836[0.146]0.269[0.614]0.00293***[0.00113]
0.00901[0.00805]
0.000265[0.00289]

Mother Employedduring child's earlyyears

0.571***[0.0264]0.573***[0.0263]0.0393[0.0254]-0.00916[0.0304]-0.0883***[0.0339]-0.0153[0.0559]-0.0136[0.0629]0.0907[0.0838]0.00484[0.00650]-0.00817**[0.00324]
0.0354[0.0370]-0.0727[0.0790]0.00896[0.0296]0.00339[0.00622]
0.0295[0.0267]-0.00002[0.00005]
0.000811***[0.000261]
0.000632***[0.000101]
0.509***[0.0297]

1.843***[0.124]

1.853***[0.124]0.154*[0.0920]-0.0137[0.107]-0.296**[0.125]-0.0191[0.184]0.0182[0.217]0.398[0.277]0.0111[0.0237]-0.0249**[0.0121]0.117[0.135]-0.266[0.305]0.0635[0.107]0.0239[0.0236]
0.158[0.0976]-1.01E-04[0.000178]
0.00294*[0.00151]
0.00312***[0.000513]
1.679***[0.130]Father during child's earlyyears: Regular
employment-0.195***-0.186***-0.162***-0.688***-0.660***-0.580***[0.0599][0.0602][0.0610][0.201][0.202][0.204]

Father during child's earlyyears: Self-employed-0.00431-0.00614

0.00537

0.03950.03570.0438[0.0661][0.0661][0.0667][0.227][0.228][0.229]EldestTwo siblings
Three siblings
Four or more siblings
Child's age
Mother's age
Mother: University
degreeFatherless family
Father: University degree
Number of students perteacher
JHPS
Household income
School expenditure
Mother's income
Nursery school

Employment rate of
women in the living area
Number of children
during child's early years:
2 children

0.00716[0.00656]

0.00805[0.00654]

0.005[0.00649]

0.0251[0.0220]0.0276[0.0221]0.0126[0.0226]-0.122***-0.123***-0.129***-0.404***-0.407***-0.468***[0.0382][0.129][0.0381][0.0375][0.125][0.126]

Number of children
during child's early years:
3 children-0.0733-0.0827-0.0776-0.367-0.399-0.352[0.0867][0.0871][0.0855][0.322][0.322][0.326]

Number of children
during child's early years:
4 children

0.032

0.03870.0128

0.197

0.217

0.0373[0.0942][0.0947][0.0929][0.349][0.349][0.356]

Constant

45.03***

0.421

45.04***

0.37

45.01***

0.48

45.29***-0.28

45.31***-0.43645.35***-0.0991[4.552][0.355][4.547][0.355][4.560][0.352][4.350]0.150*[0.0833]
2.236***[0.0224][1.214][4.347]0.149*[0.0830]
2.236***[0.0224][1.219][4.362]0.161*[0.0921]2.236***[0.0226][1.216]athrholnsigma
Wu-Hausman F test
Overidentification test
Underidentification
test
First stage F test
Partial R squared
LR test (H0:ρ=0)
Log likelihood

2.116
1.577

2.008
1.59

281.84***282.28***103.80***
0.106

1.505
1.579
203.24***103.84***
0.324

0.107

63.50***
0.328

0.108

0.349
3.30*
-4336

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1.

24

3.27*
-4333

3.11*
-4312

Table 3-2: Estimation Results of Math Test Scores (OLS, IV, TEM, n = 1,044)
OLS

IV

TEM

OLS

IV

TEM

OLS

IV

TEM

Mother Employed duringchild's early years-1.121*-2.426**-2.760**-1.157*-2.429**-2.781**-0.964-2.343*-2.862**[0.603][1.137][1.085][0.605][1.137][1.081][0.651][1.357][1.253]

HouseholdAnnual income

0.00290**0.00305***

0.0031***0.00281**0.00293**0.003***

0.00318**0.00300**

0.0029***[0.00114][0.00115][0.00104][0.00114][0.00114][0.00104][0.00124][0.00123][0.00113]

0.00751

0.00865

0.008960.00751

0.00860.00901[0.00770][0.00758][0.00804][0.00775][0.00759][0.00805]

Educational
expenditure
Mother's income
Child's information
Mother's/father's
academic backgroundArea-0.00228-0.00043

0.000265[0.00237][0.00275][0.00289]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The estimations include child’s
information (girl, born in the last three months of school year, eldest child, two siblings, three siblings, four or more siblings, child’sage), mother’s and father’s academic backgrounds (mother graduated from university, father graduated from university, fatherlessfamily), and area (number of students per teacher, JHPS).

Table 3-3: Estimation Results of Japanese Language Test Scores(OLS, IV, TEM, n = 1,044)
Mother Employed duringchild's early years
HouseholdAnnual income

OLS

IV

TEM

OLS

IV

TEM

OLS

IV

TEM

-0.614-3.665***-4.112***-0.673-3.697***-4.084***-0.332-3.780***-4.018***[0.594][1.142][1.133][0.596][1.144][1.130][0.643][1.389][1.300]0.0029***0.00324***0.0033***0.0027***0.00305***

0.0031***0.0034***0.00295***

0.00292**[0.000949][0.000948][0.00105][0.000958][0.000955][0.00106][0.00105][0.00105][0.00114]

0.0122*0.0149**0.0153*0.0122*0.0149**0.0151*[0.00675][0.00663][0.00813][0.00676][0.00663][0.00813]

Educational
expenditure
Mother's income
Child's information
Mother's/father's
academic backgroundArea-0.00402*

0.000601

0.00092[0.00226][0.00272][0.00295]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The estimations include child’s
information (girl, born in the last three months of school year, eldest child, two siblings, three siblings, four or more siblings, child’sage), mother’s and father’s academic backgrounds (mother graduated from university, father graduated from university, fatherlessfamily), and area (number of students per teacher, JHPS).

25

Table 3-4: Estimation Results of Reasoning Test Scores (OLS, IV, TEM, n = 1,044)
Mother Employed duringchild's early years
HouseholdAnnual income

OLS

IV

TEM

OLS

IV

TEM

OLS

IV

TEM

-0.0548-0.297**-2.094*-0.0603-0.296*-2.083*-0.0522-0.339*-2.206*[0.0814][0.151][1.133][0.0818][0.151][1.130][0.0874][0.183][1.317]0.000169

0.000197

0.001510.0001550.0001790.001410.000171

0.000134

0.00111[0.000153][0.000155][0.00108][0.000153][0.000155][0.00108][0.000171][0.000171][0.00116]

0.001120.00133

0.007750.001120.00135

0.00772[0.000949][0.000966][0.00831][0.000944][0.000985][0.00832]

Educational
expenditure
Mother's income
Child's information
Mother's/father's
academic backgroundArea-0.0001

0.000290.00182[0.000328][0.000392][0.00300]

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The estimations include child’s
information (girl, born in the last three months of school year, eldest child, two siblings, three siblings, four or more siblings, child’sage), mother’s and father’s academic backgrounds (mother graduated from university, father graduated from university, fatherlessfamily), and area (number of students per teacher, JHPS).

Table 3-5: Coefficients of Employment during Child’s First Three Years Dummy (school results asviewed by the parent, problem behavior, prosocial behavior, QOL)
School's scores as viewed byparent: Mathematics
School's scores as viewed byparent: Japanese language
School's scores as viewed byparent: English language
Problem behavior

OLS-0.0496[0.0710]0.023[0.0641]
0.0635[0.142]0.103[0.346]IV-0.129[0.141]-0.135[0.130]0.1[0.282]
1.383*[0.741]TEM-0.144[0.133]-0.118[0.124]0.0184[0.307]1.344**[0.681]

0.0994-0.224-0.219[0.144][0.301][0.290]1.749*[1.001]0.449[2.261]-0.54[2.032]

Prosocial behavior
QOL

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The estimations include
household income, educational expense and mother’s income in addition to child’s information (girl, born in the last three months ofschool year, eldest child, two siblings, three siblings, four or more siblings, child’s age), mother’s and father’s academic
backgrounds (mother graduated from university, father graduated from university, fatherless family), and area (number of students
per teacher, JHPS).

26

Table 3-6: Estimation by School Grade Group (three grades per group,) Coefficients of
Employment during First Three Years Dummy (QOL estimated by OLS, others
estimated by IV and TEM)IV
School grade
Mathematics
Japanese language
Reasoning
Sample size
Problem behavior
Sample size
QOL
Sample size

El 1-3-4.722**[2.161]-5.193**[2.213]-1.003***[0.331]363
0.943[1.126]362-

El 4-6-1.447[2.023]-3.774*[2.222]-0.201[0.284]361
0.741[1.144]361-

TEM
Jun 1-3-0.768[2.703]-2.156[2.508]0.475*[0.288]320
1.476[1.530]317-

El 1-3-3.845*[2.021]-6.744***[2.263]-1.214***[0.322]363
1.12[1.122]362-

OLS

El 4-6-3.758[2.465]-5.533***[2.003]-0.0726[0.282]361
0.429[1.087]361-

Jun 1-3-0.965[2.252]-1.175[2.512]0.356[0.255]320
1.21[1.312]317-

El 1-3
1.205[2.865]143

El 4-6
2.51[1.554]344

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The“El” means elementary
school; the “Jun” means Junior high school. The estimations include household income, educational expense and mother’s income
in addition to child’s information (girl, born in the last three months of school year, eldest child, two siblings, three siblings, four ormore siblings, child’s age), mother’s and father’s academic backgrounds (mother graduated from university, father graduated fromuniversity, fatherless family), and area (number of students per teacher, JHPS).

Table 3-7: Overall Estimation by School Grade Group, Coefficients of Regular
Employment during Child’s First Three Years Dummy (OLS)
Mathematics
Japanese language
Reasoning
Sample size

Overall

El 1-3El 4-6

Jun 1-3-3.433***-5.257**-3.435-1.471[1.278][2.145][2.556][2.035]-2.729**-2.908-5.911*-0.815[1.379][2.124][3.334][1.721]-0.102-0.539*-0.1030.377*[0.169][0.276][0.389][0.223]

1044

363

361

320

Note: The figure inside the [ ] is the standard deviation. *** = p < 0.01, ** = p < 0.05, * = p < 0.1. The “El” means elementary
school; the “Jun” means Junior high school. The estimations include household income, educational expense, and mother’s income,in addition to child’s information (girl, born in the last three months of school year, eldest child, two siblings, three siblings, four ormore siblings, child’s age), mother’s and father’s academic backgrounds (mother graduated from university, father graduated fromuniversity, fatherless family), and area (number of students per teacher, JHPS).

27

Jun 1-3
1.542[1.470]313

Section 4: Conclusion
This study empirically analyze the impact of maternal employment in early childhoodon the child’s test scores, the child’s results as viewed by the parent, the child’s problem
behavior, the child’s prosocial behavior, and the child’s QOL. Conducting IV and TEM
analyses that took maternal employment in early childhood into account reveal thatmaternal employment in early childhood is negatively correlated in particular with thechild’s test scores. There is an especially strong correlation when the mother had
worked continuously as a regular employee during the child’s first three years. However,according to the estimation conducted by school grade group (three grades per group),
this effect progressively diminish in the higher school grades and the negative
correlation disappear by junior high school. Regarding school’s scores as viewed by theparent and child’s prosocial behavior, there is no significant correlation in the OLS, IV,
and TEM tests. Regarding child’s QOL, although there is a significant correlation in the
OLS test, in the estimation conducted by school grade group as well as in the IV andTEM tests that controlled endogeneity, there is no correlation. Regarding child’s
problem behavior, the IV and TEM tests show that the scores for problem behavior are
higher when the mother had worked during the child’s early years, but no suchcorrelation is shown in the estimation conducted by school grade.
These results suggest that maternal employment in early childhood may have a
temporary negative effect on the child’s school results during lower grades of
elementary school education, but that this effect vanishes. Mothers who worked during
the child’s early years spent more on their child’s education for all school grades than28

did mothers who were full-time housewives. This suggests that whereas maternal
employment in early childhood may have a temporary negative impact on the child’s
elementary school results, this effect will then be potentially offset by, for instance, thesubsequent use of educational resources.
Because this study made use of retrospective data, the analysis is conducted only by
the mother’s type of employment. However, other factors such as the actual hours ofwork or the mother’s work description during the child’s early years are considered to
be important factors as well. Furthermore, in terms of whether there was someone otherthan the mother available to raise the child, there is also a need to investigate situations
such as three-generation households, personal networks, and social capital. KHPS has
carried out surveys that started in 2004. Apart from the network-related indices, the data
concerning children aged seven as of 2011 are available. However, the sample size, at
34, is very small; hence, it is difficult to identify stable tendencies. There is a need to
wait for more data to accumulate and then conduct a follow-up analysis.
Moreover, because this study used “the child was left in a nursery” as an instrumental
variable, it did not investigate the effects of nursery schools, which also have a hand inchild rearing. The instrumental variables used in this study fulfilled the necessary
conditions required for instrumental variables used in estimations. However, because it
was possible that “using nursery” and mother’s employment during the child’s earlyyears had simultaneity and “using nursery” had an impact on the child’s outcomes, thesuitability of the instrumental variables remains in doubt. In addition, there is a need in
the future to conduct more detailed investigations into this study’s results by conducting

29

further analyses using panel data that take individual characteristics into account.

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